Research And Implementation Of Recommendation System Based On Multiple Interests Of Users

With the fast development of the Internet era,making it difficult for users to quickly sift through the massive amount of information and find content of interest.Users face with information overload,recommendation systems were born to solve this problem.Based on user historical behavior and personal information,recommendation systems recommend a set of items that users may be interested in.Recommendation systems realizing personalized recommendations for everyone.A multi-interest personalized recommendation system can learn a user’s multiple different interests,upgrading the "thousand faces,thousand worlds" effect to "thousand faces,ten thousand worlds." Currently,recommendation systems have been widely used in areas such as news,travel,and e-commerce.However,there are still many problems with multi-interest recommendation systems:(1)Current mainstream multi-interest recommendation systems only consider a user’s short-term history,ignoring the rich information contained in a user’s long-term history;(2)Current mainstream recommendation system’s click-through rate prediction algorithms ignores high-order feature interactions in the user and item features or inadequately models high-order feature interactions;(3)Personalized recommendation systems have been underutilized in the field of technology resources,resulting in insufficient practical application experience.In light of the above-mentioned issues,this paper studies and implements a recommendation system based on user multi-interest.The main research work includes:(1)Designing and implementing a multiinterest recall algorithm based on user long and short-term history.Different neural network models are used to model user long and shortterm interests,and the gate fusion network is used to fuse these preferences to obtain the user’s multiple interests.The proposed algorithm achieved a 4.49%improvement in HR@50 on the MovieLens dataset and an 8.55%improvement in HR@100 on the Taobao dataset compared to the previous best model,implement the personalized recommendation recall.(2)Studying and implementing a click-through rate prediction algorithm based on a subspace projection neural network.The model implements different high-order feature interactions in different subspaces through a subspace projection mechanism,and achieves complex feature interactions through stacked subspace projection layers.The proposed algorithm outperforms the previous best model on the Criteo and Avazu datasets,and the importance of each feature is shown through visualization,implement the personalized recommendation sort.(3)Based on the above research,a personalized recommendation system for the technology resource domain is designed and implemented.The system includes features such as page interaction,model training and deployment,account management,and detail page display,recommend technology resources personalized for users,the effectiveness of the aforementioned two algorithms has been validated in practical applications.